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Best Practices for Data Modernization Across the United States Public Health System: Scoping Review

✇Journal of Medical Internet Research
By: Zebunnesa Zeba · Stella T Lartey · Polina Durneva · Shongkour Roy · Niharika Jha · Michael Arthur Ofori · Nidhi Mittal · Stella Dockery · Nichole Saulsberry Scarboro · Michelle Taylor · Ashish Joshi
Background: The adoption of new technologies and data modernization approaches in public health aims to enhance the use of health data to inform decision-making and improve population health. However, public health departments struggle with legacy systems, siloed data, and privacy concerns, hampering new technology adoption and data sharing with stakeholders. This paper maps how to address these shortcomings by identifying data modernization challenges, initiatives, and progress. Objective: To characterize the evidence for data modernization associated gaps and best practices in public health. Methods: This scoping review was conducted using the five-stage framework developed by Arksey and O’Malley and was reported according to the PRISMA-ScR guidelines. A structured search was performed in databases PubMed, Scopus, CINAHL, PsycINFO, and was complemented by a further search in the Google Scholar search engine, covering publications from January 1, 2019, to April 30, 2024. Eligible studies were peer-reviewed, published in English, and focused on data modernization initiatives within U.S. public health and reported on best practices, challenges, and outcomes. Search terms combined concepts such as “Data Modernization,” “Interoperability,” and “Public Health” using Boolean operators. Two reviewers independently screened titles, abstracts, and full texts using Rayyan QCRI, with conflicts resolved through consultation with a third reviewer. Data was extracted into Microsoft Excel and thematically analyzed. Results: This review analyzed 22 studies focused on public health data modernization. Across the literature, common components included transitioning to cloud-based systems, consolidating fragmented data into unified platforms, applying governance frameworks, and implementing analytics tools to support decision-making. Primary data sources were electronic health records, insurance claims, and disease surveillance registries. Key challenges identified across studies involved data quality issues, lack of interoperability, and limited resources, particularly in underfunded settings. Notable benefits included more timely and accessible data, improved integration across systems, and enhanced analytical capabilities, which collectively support more responsive and effective public health interventions when guided by clear standards and policy alignment. Conclusions: Progress hinges on balancing local adaptability with national coordination, improving data governance practices, and enhancing collaboration across institutions. These steps are vital to ensure public health systems can deliver timely, accurate, and actionable information to support effective public health efforts.
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Lost in Translation: Policymakers are not really listening to Citizen Concerns about AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Susan Ariel Aaronson · Michael Moreno
arXiv:2510.20568v1 Announce Type: new Abstract: The worlds people have strong opinions about artificial intelligence (AI), and they want policymakers to listen. Governments are inviting public comment on AI, but as they translate input into policy, much of what citizens say is lost. Policymakers are missing a critical opportunity to build trust in AI and its governance. This paper compares three countries, Australia, Colombia, and the United States, that invited citizens to comment on AI risks and policies. Using a landscape analysis, the authors examined how each government solicited feedback and whether that input shaped governance. Yet in none of the three cases did citizens and policymakers establish a meaningful dialogue. Governments did little to attract diverse voices or publicize calls for comment, leaving most citizens unaware or unprepared to respond. In each nation, fewer than one percent of the population participated. Moreover, officials showed limited responsiveness to the feedback they received, failing to create an effective feedback loop. The study finds a persistent gap between the promise and practice of participatory AI governance. The authors conclude that current approaches are unlikely to build trust or legitimacy in AI because policymakers are not adequately listening or responding to public concerns. They offer eight recommendations: promote AI literacy; monitor public feedback; broaden outreach; hold regular online forums; use innovative engagement methods; include underrepresented groups; respond publicly to input; and make participation easier.
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User Perceptions of Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaoyuan Wu · Roshni Kaushik · Wenkai Li · Lujo Bauer · Koichi Onoue
arXiv:2510.20721v1 Announce Type: cross Abstract: Large language models (LLMs) have seen rapid adoption for tasks such as drafting emails, summarizing meetings, and answering health questions. In such uses, users may need to share private information (e.g., health records, contact details). To evaluate LLMs' ability to identify and redact such private information, prior work developed benchmarks (e.g., ConfAIde, PrivacyLens) with real-life scenarios. Using these benchmarks, researchers have found that LLMs sometimes fail to keep secrets private when responding to complex tasks (e.g., leaking employee salaries in meeting summaries). However, these evaluations rely on LLMs (proxy LLMs) to gauge compliance with privacy norms, overlooking real users' perceptions. Moreover, prior work primarily focused on the privacy-preservation quality of responses, without investigating nuanced differences in helpfulness. To understand how users perceive the privacy-preservation quality and helpfulness of LLM responses to privacy-sensitive scenarios, we conducted a user study with 94 participants using 90 scenarios from PrivacyLens. We found that, when evaluating identical responses to the same scenario, users showed low agreement with each other on the privacy-preservation quality and helpfulness of the LLM response. Further, we found high agreement among five proxy LLMs, while each individual LLM had low correlation with users' evaluations. These results indicate that the privacy and helpfulness of LLM responses are often specific to individuals, and proxy LLMs are poor estimates of how real users would perceive these responses in privacy-sensitive scenarios. Our results suggest the need to conduct user-centered studies on measuring LLMs' ability to help users while preserving privacy. Additionally, future research could investigate ways to improve the alignment between proxy LLMs and users for better estimation of users' perceived privacy and utility.
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The Right to Be Remembered: Preserving Maximally Truthful Digital Memory in the Age of AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alex Zhavoronkov · Dominika Wilczok · Roman Yampolskiy
arXiv:2510.16206v2 Announce Type: replace Abstract: Since the rapid expansion of large language models (LLMs), people have begun to rely on them for information retrieval. While traditional search engines display ranked lists of sources shaped by search engine optimization (SEO), advertising, and personalization, LLMs typically provide a synthesized response that feels singular and authoritative. While both approaches carry risks of bias and omission, LLMs may amplify the effect by collapsing multiple perspectives into one answer, reducing users ability or inclination to compare alternatives. This concentrates power over information in a few LLM vendors whose systems effectively shape what is remembered and what is overlooked. As a result, certain narratives, individuals or groups, may be disproportionately suppressed, while others are disproportionately elevated. Over time, this creates a new threat: the gradual erasure of those with limited digital presence, and the amplification of those already prominent, reshaping collective memory. To address these concerns, this paper presents a concept of the Right To Be Remembered (RTBR) which encompasses minimizing the risk of AI-driven information omission, embracing the right of fair treatment, while ensuring that the generated content would be maximally truthful.
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LICO: Large Language Models for In-Context Molecular Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tung Nguyen · Aditya Grover
arXiv:2406.18851v2 Announce Type: replace-cross Abstract: Optimizing black-box functions is a fundamental problem in science and engineering. To solve this problem, many approaches learn a surrogate function that estimates the underlying objective from limited historical evaluations. Large Language Models (LLMs), with their strong pattern-matching capabilities via pretraining on vast amounts of data, stand out as a potential candidate for surrogate modeling. However, directly prompting a pretrained language model to produce predictions is not feasible in many scientific domains due to the scarcity of domain-specific data in the pretraining corpora and the challenges of articulating complex problems in natural language. In this work, we introduce LICO, a general-purpose model that extends arbitrary base LLMs for black-box optimization, with a particular application to the molecular domain. To achieve this, we equip the language model with a separate embedding layer and prediction layer, and train the model to perform in-context predictions on a diverse set of functions defined over the domain. Once trained, LICO can generalize to unseen molecule properties simply via in-context prompting. LICO performs competitively on PMO, a challenging molecular optimization benchmark comprising 23 objective functions, and achieves state-of-the-art performance on its low-budget version PMO-1K.
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ScholaWrite: A Dataset of End-to-End Scholarly Writing Process

✇cs.AI, q-bio.NC updates on arXiv.org
By: Khanh Chi Le · Linghe Wang · Minhwa Lee · Ross Volkov · Luan Tuyen Chau · Dongyeop Kang
arXiv:2502.02904v4 Announce Type: replace-cross Abstract: Writing is a cognitively demanding activity that requires constant decision-making, heavy reliance on working memory, and frequent shifts between tasks of different goals. To build writing assistants that truly align with writers' cognition, we must capture and decode the complete thought process behind how writers transform ideas into final texts. We present ScholaWrite, the first dataset of end-to-end scholarly writing, tracing the multi-month journey from initial drafts to final manuscripts. We contribute three key advances: (1) a Chrome extension that unobtrusively records keystrokes on Overleaf, enabling the collection of realistic, in-situ writing data; (2) a novel corpus of full scholarly manuscripts, enriched with fine-grained annotations of cognitive writing intentions. The dataset includes \LaTeX-based edits from five computer science preprints, capturing nearly 62K text changes over four months; and (3) analyses and insights into the micro-dynamics of scholarly writing, highlighting gaps between human writing processes and the current capabilities of large language models (LLMs) in providing meaningful assistance. ScholaWrite underscores the value of capturing end-to-end writing data to develop future writing assistants that support, not replace, the cognitive work of scientists.
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LongCodeBench: Evaluating Coding LLMs at 1M Context Windows

✇cs.AI, q-bio.NC updates on arXiv.org
By: Stefano Rando · Luca Romani · Alessio Sampieri · Luca Franco · John Yang · Yuta Kyuragi · Fabio Galasso · Tatsunori Hashimoto
arXiv:2505.07897v3 Announce Type: replace-cross Abstract: Context lengths for models have grown rapidly, from thousands to millions of tokens in just a few years. The extreme context sizes of modern long-context models have made it difficult to construct realistic long-context benchmarks -- not only due to the cost of collecting million-context tasks but also in identifying realistic scenarios that require significant contexts. We identify code comprehension and repair as a natural testbed and challenge task for long-context models and introduce LongCodeBench (LCB), a benchmark to test LLM coding abilities in long-context scenarios. Our benchmark tests both the comprehension and repair capabilities of LCLMs in realistic and important settings by drawing from real-world GitHub issues and constructing QA (LongCodeQA) and bug fixing (LongSWE-Bench) tasks. We carefully stratify the complexity of our benchmark, enabling us to evaluate models across different scales -- ranging from Qwen2.5 14B Instruct to Google's flagship Gemini model. We find that long-context remains a weakness for all models, with performance drops such as from 29% to 3% for Claude 3.5 Sonnet, or from 70.2% to 40% for Qwen2.5. The LCB dataset is available publicly at https://huggingface.co/datasets/Steefano/LCB and the codebase to replicate the work on this paper at https://github.com/Zteefano/long-code-bench.
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Exploring Patient Perspectives, Engagement, and Output Quality in Doctor-Supervised Use of Artificial Intelligence During Informed Consent Consultation With ChatGPT and Retrieval Augmented Generation (RAG): Quantitative Exploratory Study

✇Journal of Medical Internet Research
By: Sascha Donner · Poroshista Knauer · Arne Kienzle · Jesse Dinneen · Joost Burger · Carsten Perka · Stefanie Donner
Background: Comprehensive preoperative education is essential for optimizing outcomes and ensuring informed consent in patients undergoing total hip arthroplasty (THA). Emerging artificial intelligence (AI) tools, such as ChatGPT, offer scalable support for patient education, but their clinical application requires rigorous evaluation to ensure accuracy, safety, and trust. Objective: This study assessed patients’ preferences and satisfaction with AI-assisted informed consent in THA, comparing traditional physician consultations to those supported by native ChatGPT and a customized version enhanced with retrieval-augmented generation (RAG). It also examined how state anxiety and general attitudes toward AI affect preferences for AI-supported consent and whether RAG integration improves ChatGPT response quality. Methods: A total of 36 patients scheduled for elective THA were assigned to one of three groups (12 each): (1) standard physician-only consultations (control), (2) physician-assisted consultations supported by native ChatGPT, and (3) supported by ChatGPT enhanced through RAG. Data collection involved standardized Likert scale questionnaires assessing patient satisfaction with the consent process, perceived informedness, anxiety levels, and attitudes toward AI. The ChatGPT responses were independently evaluated by physicians for relevance, accuracy, clarity, completeness, adherence to evidence-based guidelines, and appropriate length. Instances of hallucinations, factually incorrect or misleading outputs, were identified and rated by severity. Statistical analyses compared outcomes across groups and explored associations. Results: Patients interacting with the ChatGPT+RAG model reported significantly higher satisfaction levels with information delivery (P=.01) and perceived level of informedness (P=.01) than those using the native ChatGPT model. The mean number of patient questions in the control group was 20, compared with 39 in the native ChatGPT group (P=.06) and 52 in the ChatGPT+RAG group (P=.002). The majority of participants across all groups preferred a human clinician providing less accurate information over a more accurate AI-only assistant. These preferences were not influenced by sociodemographic variables (age, gender, and education), health literacy, state anxiety, or general attitudes toward AI. The ChatGPT+RAG model outperformed the native ChatGPT model across all evaluated response quality dimensions (all P<.01) and exhibited a significantly lower hallucination rate (5/52, 10% versus 15/39, 38%; P=.002). Conclusions: Integrating RAG with ChatGPT significantly improves the quality, clarity, and reliability of preoperative information, enhancing patient satisfaction and engagement beyond native ChatGPT. However, patients maintain a strong preference for physician-led informed consent, underscoring the role of AI chatbots as complementary tools rather than replacements. These findings support the cautious adoption of customized AI assistants to augment, not substitute, human interaction in surgical consent processes. Trial Registration:
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Global Adoption, Promotion, Impact, and Deployment of AI in Patient Care, Health Care Delivery, Management, and Health Care Systems Leadership: Cross-Sectional Survey

✇Journal of Medical Internet Research
By: Obinna O Oleribe · Andrew W Taylor-Robinson · Vetty R Agala · Olajide O Sobande · Ricardo Izurieta · Simon D Taylor-Robinson
Background: Artificial intelligence (AI) is increasingly being integrated into health care, offering a wide array of benefits. Current AI applications encompass patients’ diagnosis, treatment, data mining, and more to enhance patient care and quality of life. It is also democratizing access to expert support by providing timely and accurate disease diagnoses, better clinical management, quicker drug discovery, improved disease prevention, big data management, and health protection. Objective: The aim of the study is to document AI adoption in health care, assess participants’ perception on its usefulness in the management of health care delivery and leadership of health care systems, and identify characteristics of early adopters. Methods: We conducted a worldwide cross-sectional survey across all 6 inhabited continents using a self-administered questionnaire developed with the Qualtrics electronic data collection tool. This was piloted and reviewed to ensure completeness, accuracy, acceptability, cultural sensitivity, and relevance. Respondents were recruited by individualized email, following identification from professional associations or organizations, professional networks, and social media. Data were analyzed using SPSS (IBM Corp), with results presented as narrative, charts, and tables. Results: In total, 506 health care professionals completed the survey. While 92.3% (467/506) of respondents believed that AI has a role in patient care and health care management, only 76.5% (300/392) were willing to support AI adoption and embedding in their organization. Although top managers are mainly responsible for adoption processes, staff training remains low. AI is currently used mostly for diagnosis, patient care, and precision medicine. These uses of AI will continue in the near future, but in different ways. AI adoption was highest in Europe and lowest in Africa. Black or African American people were more likely to support AI adoption than White and Asian people. Poor knowledge of AI, fear of job loss, and resistance to change were the top barriers to AI adoption and embedding. Conclusions: AI use in health is global, but the adoption rate varies by geography and individual characteristics. AI adoption communication by executive health care management is poor, as is the level of training of health care staff. To improve AI adoption, management should improve communication with their teams, provide training on AI to their workers, and help individuals understand how AI works. Barriers such as ethical issues around data ownership and use should be addressed. African organizations should be proactive and invest in AI adoption early, so that they are not left behind in the AI revolution.
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STAT+: Duke data scientist launches startup to help hospitals adopt AI

✇STAT
By: Casey Ross

Mark Sendak was getting tired of seeing the toil of so many colleagues go to waste.

At Duke University, he was part of a team of data scientists and engineers who built artificial intelligence tools to help make better health care decisions, and to more effectively treat patients with serious and life-threatening conditions. 

But even when one of their inventions appeared to help patients and generated positive results in scientific studies, it never gained uptake beyond Duke’s walls. Patients and doctors in other health systems didn’t get the opportunity to benefit.

Continue to STAT+ to read the full story…

© Courtesy Vega Health

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Comprehensive bioinformatics analysis of omics data to reveal molecular mechanisms and biomarkers in multiple cancers

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Mijan Mia · Tonima Rahman Tuli · Kazi Musfika Binte Absar · Arifa Afrose Rimi · Sumiya Ahmad Sejuti · Adrita Noumi Arshi · Md Emdadul Islam · Ahsan Habib

In Silico Pharmacol. 2025 Oct 17;13(3):154. doi: 10.1007/s40203-025-00440-3. eCollection 2025.

ABSTRACT

Breast, ovarian, lung, cervical, and colorectal cancers are among the most prevalent malignancies affecting women worldwide. This study aimed to elucidate the common molecular mechanisms of tumorigenesis and identify potential biomarkers using an integrative bioinformatics and network-based approach. Integrative profiling of five microarray datasets identified 66 differentially expressed genes (DEGs) that are common across five cancer types. Gene ontology and KEGG pathway analyses of common DEGs were performed using the DAVID database. The cell cycle processes were the most enriched functions, and oocyte meiosis, oocyte maturation, the p53 signaling pathway, cancer pathways, and cellular senescence were the most important pathways identified. Protein-protein interaction (PPI) networks for the DEGs were constructed using the STRING database, and the resulting networks were visualized in Cytoscape. Through PPI network analysis, ten hub genes were identified, and subsequent survival analysis confirmed that CHEK1, DLGAP5, CCNB2, and CCNA2 are significantly associated with poor patient survivability, establishing them as common biomarkers across multiple cancer types. Subsequently, ten transcription factors (TFs) and ten post-transcriptional regulators were identified through the assessment of regulatory networks involving TFs-DEGs and miRNAs-DEGs. Finally, drug-gene association analysis from the GSCA library was used to anticipate drug-like compounds using the drug repurposing approach. Overall, this comprehensive investigation holds promise for future in vitro and in vivo studies, offering a molecular foundation for the diagnosis, prognosis, and treatment of malignant cancers.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00440-3.

PMID:41113171 | PMC:PMC12534660 | DOI:10.1007/s40203-025-00440-3

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Alternatives to animal testing are the future — it’s time that journals, funders and scientists embrace them

✇Nature - Issue - nature.com science feeds
By: Todd J. Herron · Anke Brüning-Richardson · Julie E. Gough · Aline F. Miller · Geoffrey J. Pilkington · John Greenman · Valerie Speirs · Joseph C. Wu

Nature, Published online: 20 October 2025; doi:10.1038/d41586-025-03344-6

Biomedical research techniques that don’t involve the use of animals are gaining momentum, but those using innovative approaches still face resistance from some quarters.
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Circulating tumor DNA in Non-Viral head and neck squamous cell Carcinoma: A systematic review and Meta-Analysis

✇MRD
By: Vanessa Helou · Nana-Hawwa Abdul-Rahman · Suet Kee Loo · Shou-Jiang Gao · Jose P Zevallos · Dan P Zandberg · Matthew E Spector · Heath D Skinner · Robert L Ferris · Kevin J Contrera

Oral Oncol. 2025 Nov;170:107760. doi: 10.1016/j.oraloncology.2025.107760. Epub 2025 Oct 17.

ABSTRACT

Non-viral head and neck squamous cell carcinoma (HNSCC) has poor survival and high recurrence rates. Circulating tumor DNA (ctDNA) is a promising biomarker for understanding tumor biology, assessing treatment response, and monitoring disease progression. While extensively studied in virally mediated HNSCC, its role in non-viral HNSCC remains underexplored. This systematic review and meta-analysis consolidates evidence on the diagnostic, prognostic, and therapeutic value of ctDNA in non-viral HNSCC. A systematic search across Medline, PubMed, Embase, and the Cochrane Library identified 1,915 records, of which 47 were included. Data extraction followed PRISMA guidelines, with overall survival (OS), progression-free survival (PFS), and recurrence-free survival (RFS), pooled as hazard ratios (HRs) with 95% confidence intervals (CIs) using a fixed-effect model. Among 3,574 patients, the most common tumor sites were the oral cavity (35 %) and oropharynx (22 %), with the majority presenting with stage IVA/IVB disease (29 %). Pre-treatment ctDNA detection rates ranged from 50 % to 100 % (median: 83 %), while post-treatment detection rates varied between 28 % and 100 % (median: 48 %). ctDNA detected recurrence in 80 % of patients, with a median lead time of 4.6 months. ctDNA detection was significantly associated with worse OS (HR 10.26, 95 % CI 3.58-29.40; P < 0.0001). Residual ctDNA was strongly correlated with worse PFS (HR 7.32, 95 % CI 4.17-12.86; P < 0.00001) and RFS (HR 7.33, 95 % CI 2.75-19.58; P < 0.0001). ctDNA holds potential for improving diagnostic accuracy, monitoring progression, and predicting survival outcomes in non-viral HNSCC. However, further large-scale studies and standardized guidelines are needed for validation and clinical implementation.

PMID:41108912 | DOI:10.1016/j.oraloncology.2025.107760

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Evidence of fructose metabolism in colorectal cancer

✇Cell Death Discovery nature.com science feeds
By: Giuseppe Sigismondo Sica · Julia Bischof · Lukas Funke · Juhl Hartmut · Eleonora Candi · Alessandro Mauriello · Manuel Scimeca · Xinyue Gao · Luca Savino · Francesca Servadei · Ruigang Yang · Lu Wang · Qing Zhao · Wen-Lian Chen · Qiang Sun · Wei Jia · Gerry Melino

Cell Death Discovery, Published online: 16 October 2025; doi:10.1038/s41420-025-02745-w

Evidence of fructose metabolism in colorectal cancer
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Codon specific readthrough as a mechanism of BRCA2 restoration in acquired PARP inhibitor and chemotherapy resistance

✇(Multiomics OR Omics) AND (Pancreatic)
By: Cordelia D McGehee · X Wei Meng · Xinyan Wu · Cristina Correia · Annapoorna Venkatachalam · Karen S Flatten · Kevin L Peterson · Anand G Patel · Olivia K Rossman · Clara Wong · Rachel M Hurley · Jill M Wagner · Paula A Schneider · Haiming Dai · Guy G Poirier · Andrea E Wahner Hendrickson · Elizabeth M Swisher · Akhilesh Pandey · Hu Li · Scott H Kaufmann

Nucleic Acids Res. 2025 Oct 14;53(19):gkaf990. doi: 10.1093/nar/gkaf990.

ABSTRACT

BRCA2 mutations contribute to the pathogenesis and treatment sensitivity of a subset of ovarian, breast, prostate, and pancreatic cancers. When these cancers become therapy resistant, secondary mutations that restore the BRCA2 open reading frame are found in half the cases, but other causes of resistance remain incompletely understood. Here, we identified translational readthrough of a premature termination codon (PTC) as a cause of resistance to poly(ADP-ribose) polymerase inhibitors (PARPis) and cisplatin in cells derived from the BRCA2-mutated ovarian cancer line PEO1 by PARPi selection. Despite persistence of the signature 4965C > G (p.Y1655X) BRCA2 mutation, low-level expression of full-length BRCA2 protein was detectable in these cells by immunoblotting and tandem mass spectrometry. Either BRCA2 knockdown or gene interruption 5' or 3' to the PTC restored treatment sensitivity, implicating BRCA2 in the resistance. Reporter assays demonstrated UAG-selective readthrough in the resistant clones but not parental cells. Moreover, custom searching of global proteomic data indicated readthrough of stop codons, particularly UAGs, in additional proteins in the resistant clones. Finally, multi-omic analysis identified multiple changes in the nonsense-mediated decay and termination machineries that favor readthrough. Accordingly, the present results identify PTC readthrough as a potential mechanism of drug resistance in cells with BRCA2 nonsense mutations.

PMID:41099700 | PMC:PMC12526053 | DOI:10.1093/nar/gkaf990

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